Let agents write SQL. Validate the answer before it ships.
Vuon combines SQL generation with semantic checks, execution validation, and replayable calculations so plausible analysis does not pass as true analysis.
Check the candidate query
The agent receives schema and definition feedback before execution.
Execute against the warehouse
Vuon runs the query with scoped credentials and records the result state.
Test the output
The system checks the answer before presenting it as a decision-ready result.
Reliability layer
Compile, execute, validate, replay
A harness around model output
Better models help, but enterprise analysis needs a system that can catch fluent wrongness before a stakeholder acts on it.
Pre-execution checks
Candidate SQL is checked against known schemas, metric definitions, and policy context.
Post-execution validation
Vuon inspects outputs for structural and statistical signs that the query is wrong.
Visible correction loops
When the agent has to revise its approach, the correction path remains part of the record.
Trust that scales past demos
The goal is not to prevent agents from writing SQL. The goal is to make the generated work testable enough for production decisions.
Definition adherence
Metric logic is treated as operational context rather than decorative prompt text.
Intermediate state
Calculations are tracked as artifacts that can be reused, inspected, and rerun.
Warehouse-native execution
Validation happens against the same data platform your team already governs.
How it works
A clear path from context to action.
The same operating rhythm runs through the product: gather trusted context, show the work, then ship an output that can be reviewed.
Check the candidate query
The agent receives schema and definition feedback before execution.
Execute against the warehouse
Vuon runs the query with scoped credentials and records the result state.
Test the output
The system checks the answer before presenting it as a decision-ready result.
Next step